Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors
Previous studies suggest an impact of body composition on outcome in melanoma patients. We aimed to determine the prognostic value of CT-based body composition assessment in patients receiving immune checkpoint inhibitor therapy for treatment of metastatic disease using a deep learning approach. One...
Autores principales: | , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700660/ https://www.ncbi.nlm.nih.gov/pubmed/34943551 http://dx.doi.org/10.3390/diagnostics11122314 |
_version_ | 1784620810875437056 |
---|---|
author | Faron, Anton Opheys, Nikola S. Nowak, Sebastian Sprinkart, Alois M. Isaak, Alexander Theis, Maike Mesropyan, Narine Endler, Christoph Sirokay, Judith Pieper, Claus C. Kuetting, Daniel Attenberger, Ulrike Landsberg, Jennifer Luetkens, Julian A. |
author_facet | Faron, Anton Opheys, Nikola S. Nowak, Sebastian Sprinkart, Alois M. Isaak, Alexander Theis, Maike Mesropyan, Narine Endler, Christoph Sirokay, Judith Pieper, Claus C. Kuetting, Daniel Attenberger, Ulrike Landsberg, Jennifer Luetkens, Julian A. |
author_sort | Faron, Anton |
collection | PubMed |
description | Previous studies suggest an impact of body composition on outcome in melanoma patients. We aimed to determine the prognostic value of CT-based body composition assessment in patients receiving immune checkpoint inhibitor therapy for treatment of metastatic disease using a deep learning approach. One hundred seven patients with staging CT examinations prior to initiation of checkpoint inhibition between January 2013 and August 2019 were retrospectively evaluated. Using an automated deep learning-based body composition analysis pipeline, parameters for estimation of skeletal muscle mass (skeletal muscle index, SMI) and adipose tissue compartments (visceral adipose tissue index, VAI; subcutaneous adipose tissue index, SAI) were derived from staging CT. The cohort was binarized according to gender-specific median cut-off values. Patients below the median were defined as having low SMI, VAI, or SAI, respectively. The impact on outcome was assessed using the Kaplan–Meier method with log-rank tests. A multivariable logistic regression model was built to test the impact of body composition parameters on 3-year mortality. Patients with low SMI displayed significantly increased 1-year (25% versus 9%, p = 0.035), 2-year (32% versus 13%, p = 0.017), and 3-year mortality (38% versus 19%, p = 0.016). No significant differences with regard to adipose tissue compartments were observed (3-year mortality: VAI, p = 0.448; SAI, p = 0.731). On multivariable analysis, low SMI (hazard ratio (HR), 2.245; 95% confidence interval (CI), 1.005–5.017; p = 0.049), neutrophil-to-lymphocyte ratio (HR, 1.170; 95% CI, 1.076–1.273; p < 0.001), and Karnofsky index (HR, 0.965; 95% CI, 0.945–0.985; p = 0.001) remained as significant predictors of 3-year mortality. Lowered skeletal muscle index as an indicator of sarcopenia was associated with worse outcome in patients with metastatic melanoma receiving immune checkpoint inhibitor therapy. |
format | Online Article Text |
id | pubmed-8700660 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87006602021-12-24 Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors Faron, Anton Opheys, Nikola S. Nowak, Sebastian Sprinkart, Alois M. Isaak, Alexander Theis, Maike Mesropyan, Narine Endler, Christoph Sirokay, Judith Pieper, Claus C. Kuetting, Daniel Attenberger, Ulrike Landsberg, Jennifer Luetkens, Julian A. Diagnostics (Basel) Article Previous studies suggest an impact of body composition on outcome in melanoma patients. We aimed to determine the prognostic value of CT-based body composition assessment in patients receiving immune checkpoint inhibitor therapy for treatment of metastatic disease using a deep learning approach. One hundred seven patients with staging CT examinations prior to initiation of checkpoint inhibition between January 2013 and August 2019 were retrospectively evaluated. Using an automated deep learning-based body composition analysis pipeline, parameters for estimation of skeletal muscle mass (skeletal muscle index, SMI) and adipose tissue compartments (visceral adipose tissue index, VAI; subcutaneous adipose tissue index, SAI) were derived from staging CT. The cohort was binarized according to gender-specific median cut-off values. Patients below the median were defined as having low SMI, VAI, or SAI, respectively. The impact on outcome was assessed using the Kaplan–Meier method with log-rank tests. A multivariable logistic regression model was built to test the impact of body composition parameters on 3-year mortality. Patients with low SMI displayed significantly increased 1-year (25% versus 9%, p = 0.035), 2-year (32% versus 13%, p = 0.017), and 3-year mortality (38% versus 19%, p = 0.016). No significant differences with regard to adipose tissue compartments were observed (3-year mortality: VAI, p = 0.448; SAI, p = 0.731). On multivariable analysis, low SMI (hazard ratio (HR), 2.245; 95% confidence interval (CI), 1.005–5.017; p = 0.049), neutrophil-to-lymphocyte ratio (HR, 1.170; 95% CI, 1.076–1.273; p < 0.001), and Karnofsky index (HR, 0.965; 95% CI, 0.945–0.985; p = 0.001) remained as significant predictors of 3-year mortality. Lowered skeletal muscle index as an indicator of sarcopenia was associated with worse outcome in patients with metastatic melanoma receiving immune checkpoint inhibitor therapy. MDPI 2021-12-09 /pmc/articles/PMC8700660/ /pubmed/34943551 http://dx.doi.org/10.3390/diagnostics11122314 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Faron, Anton Opheys, Nikola S. Nowak, Sebastian Sprinkart, Alois M. Isaak, Alexander Theis, Maike Mesropyan, Narine Endler, Christoph Sirokay, Judith Pieper, Claus C. Kuetting, Daniel Attenberger, Ulrike Landsberg, Jennifer Luetkens, Julian A. Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors |
title | Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors |
title_full | Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors |
title_fullStr | Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors |
title_full_unstemmed | Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors |
title_short | Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors |
title_sort | deep learning-based body composition analysis predicts outcome in melanoma patients treated with immune checkpoint inhibitors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700660/ https://www.ncbi.nlm.nih.gov/pubmed/34943551 http://dx.doi.org/10.3390/diagnostics11122314 |
work_keys_str_mv | AT faronanton deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors AT opheysnikolas deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors AT nowaksebastian deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors AT sprinkartaloism deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors AT isaakalexander deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors AT theismaike deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors AT mesropyannarine deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors AT endlerchristoph deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors AT sirokayjudith deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors AT pieperclausc deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors AT kuettingdaniel deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors AT attenbergerulrike deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors AT landsbergjennifer deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors AT luetkensjuliana deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors |